Video camera having an adaptive automatic iris control circuit

ABSTRACT

A video camera includes a lens, an iris and an iris driving circuit, an image sensor, a circuit for dividing the picture into a plurality of sub-areas for extracting the luminance of each sub-area according to the luminance signal provided from the image sensor as a luminance distribution signal, a circuit for generating a signal defining a target value of an iris driving signal, an adaptive circuit using an artificial neural network to which the luminance distribution signal is input for carrying out adaptive conversion so that the offset between a provided teacher signal and its own output is minimized, and a switch for selecting either the target value signal or the output of the adaptive circuit to provide the same as a teacher signal to the adaptive circuit.

This application is a continuation of application Ser. No. 07/849,673,filed on Mar. 11, 1992, U.S. Pat. No. 5,331,422 the entire contents ofwhich are hereby incorporated by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to video cameras, and more particularly,to an automatic iris control circuit for automatically adjusting theaperture size to regulate the amount of light incident into the opticalsystem of a video camera according to the incident amount of light.

2. Description of the Background Art

Referring to FIG. 1, a conventional video camera includes a lens 102 forbringing together incident light from an object to form an image on apredetermined image formation plane, an image sensing device 104 havingan image sensing plane arranged on the image sensing plane forconverting the optical image on the image sensing plane into a videosignal which is an electrical signal by photoelectric conversion, aniris plate 103 for regulating the amount of light incident upon imagesensing device 104, an iris motor 113 for driving iris plate 103, apreamplifier 116 for amplifying the video signal provided from imagesensing device 104 and providing the same into a signal processingcircuit (not shown) for converting into a television method, and anautomatic iris control circuit 217 responsive to the video signalprovided from preamplifier 116 for operating iris motor 113 so that thelevel of the video signal attains a predetermined level.

Automatic iris control circuit 217 includes a gain control amplifier(GCA) 206 for amplifying the video signal provided from preamplifier 116corresponding to the central portion of the screen by a gain that isgreater than that of the remaining portion of the screen, a voltageadder 207 for adding to the amplified video signal provided from GCA 206sawtooth voltage that will raise the voltage in the lower portion of thescreen, a detection circuit 208 for averaging the outputs of voltageadder 207, and a comparator 209 having a negative input terminalconnected to the output of detection circuit 208 and a positive inputterminal connected to a variable reference voltage (V_(IR)) 210 forcomparing the signal provided from detection circuit 208 and referencevoltage 210 to drive iris motor 113 according to the comparison output.

Referring to FIG. 2, the amplification carried out in GCA 206 is for thepurpose of giving a particularly great weight to the central portion 252of a screen 251. When object 254 is located at the central portion 252,as shown in FIG. 3A, iris plate 103 is driven to obtain an appropriateimage sensing state of object 254 owing to the amplification by GCA 206.Even if there is ground 255 in the background scenery with rear light,photometry is carried out giving weight to object 254 rather than toground 255 or the background scenery.

Referring to FIG. 2 again, voltage adder 207 adds offset voltage to thelower region 253 of screen 251. In the case where sawtooth voltage suchas that shown in FIG. 1 is added, the weight for control of iris plate103 becomes greater as a function of distance towards the bottom ofscreen 251. Therefore, when object 256 is located with the sky as thebackground in screen 251 as shown in FIG. 3B, the diaphragm is adjustedto a value that can shoot object 256 in an optimum state without beingaffected by the luminance of the sky.

Referring to FIG. 1, a conventional video camera operates as follows.The incident amount of light from object 101 forms an image on the imagesensing plane of image sensing device 104 by lens 102. Image sensingdevice 104 converts the optical image into a video signal byphotoelectric conversion. The video signal is provided to preamplifier116. Preamplifier 116 amplifies output of image sensing device 104 toprovide the same to a signal processing circuit and the input of GCA206.

As already described with reference to FIGS. 2 and 3A, GCA 206 amplifiesthe converted video signal corresponding to the central region of thescreen by a gain that is greater than that of the surrounding region.The amplified signal is provided to voltage adder 207. As described withreference to FIGS. 2 and 3B, voltage adder 207 adds sawtooth voltage tothe input signal so that the video signal representing the lower regionof the screen becomes a greater value. This signal is provided todetection circuit 208, where the video signals from voltage adder 207are averaged and provided to the negative input terminal of comparator209. Comparator 209 compares the output of detection circuit 208 andreference voltage 210 to drive iris motor 113 according to thecomparison output. Iris motor 113 drives iris plate 103 according to thedrive voltage. The amount of light of the object image upon imagesensing device 104 is adjusted by the open/close of iris plate 103.

The detailed operation of automatic iris control circuit 217 is asfollows.

If the luminance of the object image is too bright, the amplitude of theimage signal provided from preamplifier 116 becomes great. Thisincreases the average voltage of the video signal provided fromdetection circuit 208. If the output voltage of detection circuit 208becomes greater than reference voltage 210, the output of comparator 209shifts to a low potential. Iris motor 113 responds to the output ofcomparator 209 to be operative to close iris plate 103. This reduces theincident of light from the object to image sensing device 104.

If the incident amount of light from the object to image sensing device104 is low, an operation opposite to that described above is carriedout. That is to say, the output voltage of detection circuit 208decreases to become lower than reference voltage 210. The output ofcomparator 209 shifts to a high potential so that iris motor 113operates to open iris plate 103. Thus, the incident amount of light toimage sensing device 104 increases.

By the above-described operation of the automatic iris control circuit,the aperture size of iris plate 103 is adjusted to obtain a maximumluminance of an object located in the central and lower region of thescreen. The operator of the video camera does not have to manuallyadjust the aperture size to obtain a desired shooting state.

As described above, a video camera having a conventional automatic iriscontrol circuit employs center-weighted metering and foot-weightedmetering. This is based on the typical shooting condition where anobject is usually located at the central region of a screen, and thatthe sky is located above as the background with the object in the lowerregion. However, there are some cases where center-weighted metering andfoot-weighted metering may not result in an optimum diaphragm.

Consider the case where the entire background of object 254 is of highluminance in screen 251, as shown in FIG. 3C. In this case, theluminance of the lower portion of screen 251 becomes high. Iffoot-weighted metering is employed, the diaphragm will be operatedtowards the closing direction. This means that the sensed image ofobject 254 becomes dark.

Consider the case where object 254 is not located in the center portion252, as shown in FIG. 3C. In this case, most of central portion 252becomes a high luminance portion. The diaphragm will be operated towardsthe closing direction. This will also result in a very dark sensed imageof object 254.

The above-described object state is not so rare. This is oftenencountered when shooting at a ski resort, for example. In this case,the background scenery is snow, which has a very high luminance. Thestate such as shown in FIG. 3C may often be seen in ski resorts.

For users that often shoot at a ski gela/ nde, optimum aperture valuecould not be obtained by automatic iris control with the conventionalcenter-weighted metering or foot-weighted metering, resulting in anunsatisfactory picture. There are some video cameras that can adjust thediaphragm manually. However, it is very difficult to control the iriswhile shooting. There was a problem that control of the iris could notbe carried out easily with the above-described conventional videocamera.

A technique, not directed to a video camera, but to a still camera, isdisclosed in Japanese Patent Laying-Open No. 2-96724 for controlling thecamera according to the condition of the object.

Referring to FIG. 4, the still camera disclosed in Japanese PatentLaying-Open No. 2-96724 includes a lens 320, a diaphragm 319 provided infront of lens 320, and an in-focus mechanism 335 for focusing theoptical image of an object at a predetermined image formation plane bymoving lens 320 along the optical axis. The optical image of the objectis provided to an amplifier 322 as data representing the luminance ofthe object for each photoelectric converted device by an image sensingdevice 321 formed of photoelectric conversion devices allocated in amatrix manner. The luminance information of the object amplified byamplifier 322 is A/D converted by A/D converter 323 to be provided to anoperation circuit 324 as a stepped down luminance BV'. Operation circuit324 is previously input with an aperture value AV₀ representing the openaperture of diaphragm 319. Operation circuit 324 calculates and providesthe actual luminance BV (=BV'-AV₀) of the object from the two values ofBV' and AV₀. The output object luminance BV is provided to a multiplexer328 and a frame memory 334. The operation of multiplexer 328 will bedescribed afterwards.

Frame memory 334 stores object luminance BV according to the output ofeach photoelectric conversion device of image sensing device 321 foreach photoelectric conversion device. Frame memory 334 is connected to aneuro-computer 325, to which the luminance information stored in framememory 334 is supplied, and from which signal P_(xy) representing theposition of the main object of the video stored in frame memory 334 isoutput. Neuro-computer 325 is connected to a coefficient memory 326 forstoring coefficient W_(ji) to determine the operational process carriedout by neuro-computer 325. Coefficient W_(ji) is rewritten to obtain anappropriate output corresponding to the input in the learning process ofneuro-computer 325.

The output of neuro-computer 325 is connected to one input of a selector336. The output of selector 336 is connected to multiplexer 328. Theoutput of an operation panel 327 is connected to the other inputterminal of selector 336. The output of operation panel 327 is alsoconnected to neuro-computer 325. Operation panel 327 is for the purposeof providing to neuro-computer 325 a signal tp_(i) representing thelocation of the main object from the picture stored in the frame memory334 at the time of the learning mode. Operation panel 327 includes atouch panel switch (not shown), for example, having a one-to-onecorrespondence to the position of the screen.

The user inputs the position of the main object on the screen whilelooking through the finder, whereby signal tp_(i) representing theposition of the main object is provided from operation panel 327 toneuro-computer 325. Neuro-computer 325 carries out operation accordingto the coefficient stored in coefficient memory 326 by the inputprovided from frame memory 334 to temporarily determine an output.Neuro-computer 325 also compares signal t_(pi) provided from operationpanel 327 with its own output to rewrite coefficient W_(ji) incoefficient memory 326 so that the offset is minimized. By repeatingsuch learning several times, an artificial neural network implementedwith neuro-computer 325 and coefficient memory 326 is self-organized toprovide an appropriate signal P_(xy) according to the input from framememory 334.

The output of operation panel 327 is also provided to selector 336. Atthe time of learning mode, selector 336 provides the output of operationpanel 327 to multiplexer 328. At the time of automatic mode, selector326 provides the output of neuro-computer 325 to multiplexer 328.Multiplexer 328 passes only output BV of the photoelectric conversiondevice corresponding to the main object of the screen designated bycontrol signal P_(xy) provided from neuro-computer 325 or operationpanel 327. The passed output BV is provided to operation circuits 329and 331.

Operation circuit 329 carries out operation for focus-detectionaccording to the so-called hill-climbing method based on luminanceoutput BV of the photoelectric conversion device corresponding to themain object. The output of operation circuit 329 is provided to a driver330. Driver 330 moves in-focus mechanism 335 according to the suppliedoperation result to move lens 320 in its optical axis direction. Byoperation circuit 329, lens 320 stops at a location so that an image isformed on the light receiving plane of image sensing device 321.

Operation circuit 331 determines the shutter speed or the signal valueaccording to luminance BV of the main portion of the object providedfrom multiplexer 328, film sensitivity SV, aperture value AV ofdiaphragm 319, and the set shutter speed TV. The determined shutterspeed and aperture value are provided to a shutter control device 332and an iris control device 333, respectively.

Thus, the still camera disclosed in Japanese Patent Laying-Open No.2-96724 has the aperture value, the shutter speed, and the in-focusposition determined according to the luminance information of not theentire object, but only the main portion of the object. The position ofthe main object is detected by neuro-computer 325. This is carried outaccording to the learning process specified by the user throughoperation panel 327. Therefore, the detection of the position of themain portion of the object can be carried out similarly as to the likingof the user. It is described in the aforementioned Japanese PatentApplication that the main object can be photographed under anappropriate shooting state by controlling the camera according to theluminance of the main object.

However, the technique of Japanese Patent Laying-Open No. 2-96724 isdirected to a still camera. There is no disclosure as to how thistechnique is applied to a video camera. Although it is suggested inJapanese Patent Laying-Open No. 2-96724 that it is possible to provideas a teacher signal an exposure correction signal according to thebrightness or the luminance pattern of the object to such aneuro-computer for carrying out learning such as retrogressivecorrection, no specific structure is taught.

The still camera described in the embodiment of the Japanese PatentApplication which is controlled according to the luminance informationof only the main object has the following problems which will bedescribed hereinafter. The relation between the luminance information ofthe background scenery excluding the main object and the luminancesignal of the main object is critical in obtaining an optimum aperturevalue. However, it is impossible to optimize the luminance balancebetween the main object and the background with the technique disclosedin Japanese Patent Laying-Open No. 2-96724. This is not a problem inpractice for a silver salt camera represented by a still camera sinceluminance adjustment can be carried out at the time of printing.However, for a video camera, there is a limitation in carrying outluminance adjustment at the time of reproduction. The techniquedisclosed in Japanese Patent Laying-Open No. 2-96724 cannot be appliedto a video camera.

There is also another problem. It is general to use the output of theimage sensing device to obtain a video signal for the control of thediaphragm in a video camera. The number of photoelectric conversiondevices that are allocated in the image sensing device is significant tocomply with the high requirements of the picture quality. If thetechnique disclosed in Japanese Patent Laying-Open No. 2-96724 isapplied to a video camera and the output of each photoelectricconversion device is stored in a frame memory to be provided to theneuro-computer, the number of inputs for the neuro-computer will becometoo great and not appropriate for practical use. A great number ofinputs for the neuro network will result in a problem of a longer timeperiod for the learning process of the neural network. Furthermore, ifthe output of the photoelectric conversion device is directly input tothe neural network, the input value to the neural network will varygreatly in response to just a slight change in position of the object,resulting in an unstable operation of the neural network.

SUMMARY OF THE INVENTION

An object of the present invention is to provide a video camera that canautomatically control the diaphragm to an aperture value that the userfeels optimum according to a specific state of the object.

Another object of the present invention is to provide an automaticcamera that can control the diaphragm automatically and stably to anaperture value that the user feels optimum according to a specific stateof the object.

A further object of the present invention is to provide a video camerathat can automatically control the diaphragm to an aperture value thatthe user feels optimum, and that can learn to realize automaticdiaphragm that the user feels optimum for various states.

Still another object of the present invention is to provide a videocamera that can control automatically the diaphragm to an aperture valuethat the user feels optimum according to a specific state of the object,and that can carry out stably and speedily learning for realizingautomatic diaphragm that the user feels optimum for various conditions.

The video camera according to the present invention includes an opticalsystem for gathering incident light from an object for forming an imageon a predetermined image formation plane, an iris and an iris drivingcircuit for regulating the incident amount of light in response to asupplied incident light amount regulating signal, an image sensor forimage sensing an optical image of the object formed in a predeterminedframe for providing a luminance signal, and a luminance distributioncharacteristics extraction circuit connected to the image sensor todivide the frame in which the optical image is formed into a pluralityof sub-areas for extracting the luminance distribution characteristicsof the optical image in the frame as the luminance for each sub-areaaccording to the luminance signal and for providing the same as aplurality of luminance distribution signals. The video camera includes atarget value signal generating circuit for generating a target valuesignal to determine a target value of the incident light amountregulating signal that can be arbitrary set by the user according to theluminance distribution of the optical image, an adaptive circuit usingan artificial neural network to which a plurality of luminancedistribution signals are input and from which an incident light amountregulating signal obtained by a predetermined conversion is provided,for adapting conversion so that the offset of the output with respect toa provided teacher signal is minimized, and a selecting circuitresponsive to the operation of the user for selecting one of the outputof the target value signal and the adaptive circuit for providing thesame as a teacher signal to the adaptive circuit.

The image formed by the optical system in the video camera is convertedinto a luminance signal by the image sensor. The frame of a picture isdivided into plurality of sub-areas, whereby the luminance for eachsub-area is extracted from the luminance signal as the characteristicsof the luminance distribution of the picture. The extractedcharacteristics of the luminance distribution of the picture is providedto the artificial neural network of the adaptive circuit as a pluralityof luminance distribution signals. The artificial neural network appliesa predetermined conversion to the plurality of luminance distributionsignals to provide the same as an incident light amount regulatingsignal. At the time of learning of the artificial neural network, theselecting circuit provides the target value signal set by the user asthe teacher signal. The adaptive circuit has the conversion carried outinternally adapted so that the offset between the teacher signal and itsown output is minimized. At the time of normal operation, the adaptivecircuit processes the luminance distribution signals according to theconversion adapted during the learning process to provide the same tothe iris driving circuit. Therefore, the value of the incident lightamount regulating signal at the time of normal operation takes a valueaccording to the users preference which is learned based on variousstates. Optimum aperture can be obtained automatically thereafter justby a slight manual operation even in special states which could not beobtained in the conventional automatic iris control. An optimum aperturevalue taking into consideration the luminance balance between the mainobject and the background can be obtained in the artificial neuralnetwork, resulting in a picture having satisfactory balance in luminanceall over the entire screen.

The luminance distribution characteristics extraction circuit in thevideo camera of a preferred embodiment of the present invention includesan A/D converting circuit for A/D (analog-digital) converting the videosignal provided from the image sensor for providing the same as aluminance data indicating the luminance of the optical image, and anaverage luminance calculating circuit to integrate luminance data foreach sub-area by a predetermined time period for calculating the averageluminance for each sub-area to provide the same as a luminancedistribution signal.

In the video camera, the luminance data for each sub-area of the screenis extracted, whereby the average luminance thereof is calculated foreach sub-area to be provided to the adaptive circuit as the luminancedistribution signal. Because the luminance is averaged for eachsub-area, there is no great change in the luminance distribution signalwhen there is a slight movement of the object. A stable automatic iriscontrol can be realized. Furthermore, the number of inputs to theartificial neural network will not become too great.

The artificial neural network of the adaptive circuit in the videocamera according to another embodiment of the present inventionincludes: an intermediate layer having a plurality of neurons, eachcarrying out a predetermined conversion for a set of the luminancedistribution signals for obtaining one output; an output layer having aplurality of neurons, each carrying out a predetermined conversion tothe signal provided from the intermediate layer for obtaining oneoutput; an output converting circuit for carrying out a predeterminedconversion to the set of signals provided from the output layer forobtaining a single incident light amount regulating signal; and aconversion updating circuit for adaptively updating the conversioncarried out in the intermediate layer and the output layer according tothe difference between the output of the output layer and the teachersignal so that the offset thereof is minimized.

In this video camera, each neuron in the intermediate layer of theartificial neuron network carries out a predetermined conversion for theset of input luminance distribution signals for obtaining one output,which is provided to each neuron of the output layer. Each neuron in theoutput layer carries out a predetermined conversion to the signalprovided from the intermediate layer to obtain one output, which isprovided to the output converting circuit. The output converting circuitcarries out a predetermined conversion to the set of outputs of theoutput layer to provide an incident light amount regulating signal. Theconversion carried out in each converting circuit of the intermediatelayer and the output layer is adaptively updated so that the differencebetween the output of the output converting circuit and the providedteacher signal is minimized. As a result, the conversion carried out ineach converting circuit is updated according to each repetition of thelearning process of the artificial neural network, whereby learning iscarried out that realizes automatic diaphragm such that the user feelsoptimum in various states.

Each neuron in the intermediate layer and the output layer of theartificial neural network of the video camera according to a furtherpreferred embodiment of the present invention includes a weightingcircuit for giving a predetermined weight to each input signal, anadding circuit for adding all the weighted signals, and a functionconverting circuit for converting and providing the output of the addingcircuit according to a predetermined monotone increasing function. Theconversion updating circuit includes a weight updating circuit forrecalculating and updating each weight so that the root mean square ofthe offset is minimized according to the offset between the output ofthe output converting circuit and a teacher signal.

Each converting circuit of this video camera applies a predeterminedweight to the input thereof, followed by addition and a predeterminedfunction conversion to obtain one output. This weighting value issequentially recalculated and updated by the weight updating circuit sothat the root mean square of the offset between the teacher signal andthe output of the neuron network is minimized. Thus, the so-called errorbackpropagation rule is realized, whereby a stable and rapid learning iscarried out.

Further scope of applicability of the present invention will becomeapparent from the detailed description given hereinafter. However, itshould be understood that the detailed description and specificexamples, while indicating preferred embodiments of the invention, aregiven by way of illustration only, since various changes andmodifications within the spirit and scope of the invention will becomeapparent to those skilled in the art from this detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, aspects and advantages of thepresent invention will become more apparent from the following detaileddescription of the present invention when taken in conjunction with theaccompanying drawings, which are given by way of illustration only, andthus are not limitative of the present invention, and wherein:

FIG. 1 is a block diagram of a video camera having a conventionalautomatic iris control circuit.

FIG. 2 is a schematic diagram of a picture for describingcenter-weighted metering and foot-weighted metering.

FIGS. 3A-3C are schematic diagrams showing conventional photometrymethods.

FIG. 4 is a block diagram of a conventional still camera using neuralnetwork.

FIG. 5 is a block diagram showing an example of a video camera accordingto the present invention.

FIG. 6 is a schematic diagram showing an example of a divided screen.

FIG. 7 is a block diagram of an area dividing and average producingcircuit.

FIG. 8 illustrates waveform charts of horizontal area pulses fordividing the screen.

FIG. 9 illustrates waveform charts of vertical area pulses for dividingthe screen.

FIG. 10 is a block diagram of an integrating circuit.

FIG. 11 is a block diagram of a neural network.

FIG. 12 is a block diagram of a neuron of an output layer and a weightupdating circuit.

FIG. 13 is a block diagram of a neuron of the output layer.

FIG. 14 is a graph showing the characteristics of a sigmoid function ofconversion carried out in a function generator.

FIG. 15 is a block diagram of a weight calculating circuit forcalculating the weight coefficient of the neuron of the output layer.

FIG. 16 is a block diagram of a neuron of the input layer and the weightupdating circuit.

FIG. 17 is a block diagram of a neuron of the input layer.

FIG. 18 is a block diagram of a weight calculating circuit forcalculating the weight coefficient of the neuron of the input layer.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 5 is a block diagram of a video camera according to an embodimentof the present invention. Referring to FIG. 5, the video camera includeslens 102 as the optical system for gathering the incident light fromobject 1 and forming an image on a predetermined image formation plane,a CCD image sensor 104 for image sensing the optical image of object 101formed on a predetermined frame by lens 102 for converting the same intoa video signal which is an electrical signal, an iris plate 103 forregulating the incident amount of light to image sensor 104, an irismotor 113 for driving iris plate 103 according to the provided incidentlight amount regulating signal, i.e. iris control voltage, apreamplifier 116 for amplifying the video signal provided from imagesensor 104 to provide the same to a signal processing circuit, anautomatic iris control circuit 117 responsive to the level of the videosignal provided from preamplifier 116 for providing an iris controlvoltage to drive iris motor 113 according to an iris control patternlearned by the iris control process set manually by a user, and amanually operable iris control signal generating circuit 118 connectedto automatic iris control circuit 117 for providing a teacher signalthereto in the learning of the automatic iris control pattern ofautomatic iris control circuit 117.

Automatic iris control circuit 117 includes an A/D converter 105 for A/D(analog-digital) converting the luminance component of the video signalamplified by preamplifier 116, an area dividing and average producingcircuit 116 for dividing the screen into a plurality of sub-areas andintegrating the luminance data provided from A/D converter 105 for apredetermined time period for each sub-area for calculating the averageluminance to provide an average luminance signal for each sub-area, anartificial neural network (referred to as ANN hereinafter) 107responsive to the average luminance signal provided from area dividingand average producing circuit 106 for recognizing the pattern of theluminance distribution pattern in the screen to carry out conversionconforming to the iris control by the user obtained by learning forproviding in a digital value the optimum aperture size, a D/A converter108 connected to the output of ANN 107 for D/A (digital-analog)converting the digital signal representing an optimum aperture valueprovided from ANN 107, an adder 114 for adding and providing the offsetvoltage representing the offset between the aperture value which is thetarget value set by the user which is provided from manual iris controlsignal generating circuit 118 and the output of ANN 107, a comparator112 for comparing the output voltage of adder 114 and reference voltage111 for driving iris motor 113 according to the comparison output, andan A/D converter 115 for A/D converting the output of adder 114 forproviding to ANN 107 a teacher signal for learning.

Manual iris control signal generating circuit 118 includes a variableresistor 110 connected between a predetermined power supply potentialand ground potential, and a switch 109 having one input terminal 109aconnected to variable resistor 110, the other input terminal 109bconnected to ground potential, and the output terminal connected to oneinput of adder 114.

Referring to FIG. 6, area dividing and average producing circuit 106divides screen 251 into 4×4 sub-areas of a₀₀ -a₃₃, where each area isequal in size. Area dividing and average producing circuit 106 obtainsthe average luminance for each sub-region for a predetermined timeperiod to provide the same as the luminance data of each sub-region.

Referring to FIG. 7, area dividing and average producing circuit 106includes a plurality of integrating circuits 121 provided correspondingto the number of sub-areas for integrating luminance signals from one ofthe sub-areas of a₀₀ -a₃₃ (refer to FIG. 2) for a predetermined timeperiod to provide to ANN 107 an integrated value, and an area dividingtiming signal generator 122 for generating and providing to eachintegrating circuit 121 area dividing timing signals v0-v3 and h0-h3 forallowing integration of the luminance data from only the correspondingarea and a clear signal CL for clearing the integration for each screento provide the same to each integrating circuit 121.

Regarding the area dividing timing signals, signals h0-h3 are areapulses for indicating the time of dividing screen 251 (FIG. 6) along theline in the vertical direction. It is assumed that a video signal in thewaveform shown in FIG. 8(A) is provided to A/D converter 105 (FIG. 5).Referring to FIG. 8 (B), signal h0 attains a high potential from thebeginning of the video period of each horizontal scanning period. Thishigh potential of signal h0 is maintained for 1/4 of the video period.Signal h0 has a low potential in the remaining period.

Referring to FIG. 8 (C), signal h1 attains a high potential in responseto the fall of signal h0. Signal h1 maintains the high potential statefor 1/4 of the video period and attains a low potential at the remainingperiod. Referring to FIGS. 8 (D) and (E), signals h2 and h3 attain ahigh potential in response to the fall of signals h1 and h2,respectively, and maintain the high potential state for 1/4 of the videoperiod. Signals h2 and h3 are both held at a low potential in theremaining period.

Signals v0-v3 are signals indicating the timing for dividing screen 251along the line in the horizontal direction. It is assumed that the videosignal has a waveform shown in FIG. 9 (A). Referring to FIGS. 9 (B)-(E),the vertical scanning period having the region including the effectivevideo signal is divided into 4. Signals v0-v3 attain a high potential inthe first, the second, the third and the fourth period, respectively, ofthe period divided into 4. Signals v0-v3 attain a low potential in theirrespective remaining periods.

By combining one of signals h0-h3 and one of signals v0-v3, andintegrating the luminance of the video signal when both signals attain ahigh potential, the luminance data can be obtained for one of thesub-areas a₀₀ -a₃₃ shown in FIG. 6.

Integrating circuit (i, j) 121 for integrating luminance data ofsub-area a_(ij) includes an adder 124 having one input connected to A/Dconverter 105, a register 123 for storing added data provided from adder124, and a gate circuit 125 for ANDing clock signal CLK provided fromarea dividing timing signal generator 122 (refer to FIG. 7) insynchronization with the input luminance data and area pulses v_(i) andh_(j) provided from area dividing timing signal generator 122 to controlthe storing timing of register 123. The output of register 123 isconnected to one input of adder 124. The added value of the storedcontents of register 123 and the luminance data provided from A/Dconverter 105 is provided again to register 123 to be added. The clearterminal of register 123 is supplied with clear pulse CL provided foreach vertical period by area dividing timing signal generator 122. Byclear pulse CL, the contents of register 123 is cleared for each screen.

In the present embodiment, screen 251 is divided into equal sub-areas ofa₀₀ -a₃₃ shown in FIG. 6. The number of luminance data sampled from eachsub-area is identical. Therefore, the integrated value of the luminancedata obtained by register 123 can be directly processed as the luminanceaverage of the sub-area. The output 134 of integrating circuit 121 isprovided to ANN 107.

FIG. 11 is a block diagram schematically showing ANN 107. The operationmechanism of the neural network is described in, for example, "UsingNeural Network for Pattern Recognition, Signal Processing and KnowledgeProcess" Nikkei Electronics, Aug. 10, 1987, No. 427 pp. 115-124),Neuro-Computer (by Toyohiko Kikuchi, published by OHM issued in 1990),and Artificial Neural Networks-Theoretical Concepts (ed. V. Vemuri,Washington D.C, IEEE Computer Press, 1988). The details of the operationmechanism will not be described here.

Referring to FIG. 11, ANN 107 includes an intermediate layer 132 forapplying a predetermined process to luminance average data 134 providedfrom area dividing and average producing circuit 106 for obtaining anintermediate output, an output layer 136 to apply a predeterminedconversion to the output of intermediate layer 132 for obtaining nnormalized outputs, and n comparators 135 provided corresponding to eachof output layer 136 for comparing the output of output layer 136 with0.5 for providing an output of 1 or 0 according to the compared value.Intermediate layer 132 includes m neurons 131. Each is applied with allthe luminance average data 134 branched by an input layer not shown.

Output layer 136 includes n neurons 137, each having the outputconnected to the positive input terminal of the corresponding comparator135 and the input connected to the output of all the neurons 131 inintermediate layer 132. In FIG. 11, neuron 137 of the output layer isdesignated a serial number of 0 to (n-1).

ANN 107 further includes an encoder 133 for converting the number into a8-bit data allocated to the neuron 137 connected to the comparatorhaving an output of 1 and providing the same to D/A converter 108, and adecoder 130 having the input connected to A/D converter 115 (refer toFIG. 5) for decoding the signal provided from A/D converter 115 into 1bit data of t0-t255 to provide the same as a teacher signal to outputlayer 136.

Each neuron 131 of intermediate layer 132 and each neuron 137 of outputlayer 136 serve to apply a predetermined weight to each input signal andto carry out addition, followed by a predetermined function conversionto provide the same. Therefore, each of neurons 131 and 137 is providedwith a circuit for carrying out weighting for each input and a circuitfor sequentially updating the weight according to data t0-255 providedfrom decoder 130. These circuits are not shown in FIG. 11 for thepurpose of simplicity. These circuits will be described with referenceto FIGS. 12-15.

The number of neurons 137 in output layer 136 is set to 2^(a) (a is aninteger), for example 128 or 256, taking into consideration the encodingof a 8-bit data.

Each neuron 131 of intermediate layer 132 applies a predetermined weightto the input signal, followed by addition and conversion with aparticular function. Therefore, each neuron 131 is provided with aweighting circuit for applying weight to each input, and a circuit forupdating each weight coefficient according to data for learning providedfrom output layer 136. These circuits are not indicated in FIG. 11 forthe sake of simplicity. These circuits will be described in detail withreference to FIGS. 16-18.

In the ANN shown in FIG. 11, the teacher signal provided from A/Dconverter 115 is decoded by decoder 130 into one bit signals of t0-t255.The bit signal is provided to output layer 136, whereby each weightcoefficient is updated. Using the updated result, each weightcoefficient of intermediate layer 132 is updated. In other words, theflow of data at the time of normal operation and that at the time oflearning is opposite to each other. Such a learning method is callederror backpropagation rule.

FIG. 12 is a block diagram of neuron 137 of output layer 136 and aweight updating circuit 138 provided associated with neuron 137 forcalculating and updating the weight for output 139 (x₁ -x_(m)) fromintermediate layer 132 provided to neuron 137. Weight updating circuit138 is not shown in FIG. 11, as described before. Referring to FIG. 12,weight updating circuit 138 includes m weight calculating circuits 141for carrying out the weight calculation for each of input signals 139(x₁ -x_(m)).

It is assumed that neuron 137 is allocated with number j. The outputthereof is y_(j). It is clear from FIG. 12 that weight calculatingcircuit 141 corresponding to the i-th input signal 139 (x_(i))recalculates the weight coefficient according to output y_(j) of neuron137, 1 bit signal t_(j) provided from decoder 130, input signal x_(i),and the weight coefficient corresponding to input signal x_(i) includedin neuron 137 for updating the weight coefficient in neuron 137. Theoutput of weight calculating circuit 141 is provided to each neuron 131of intermediate layer 132 to be used in updating the weight coefficientof the weight applying process carried out in each neuron 131.

Referring to FIG. 13, neuron 137 of output layer 136 includes mregisters 143 for storing weight coefficients w0_(j1) -w0_(jm)corresponding to respective input signals 139 (x₁ -x_(m)), m multipliers142 for multiplying respective input signals 139 (x₁ -x_(m)) by weightcoefficients w0_(j1) -w0_(jm) stored in the corresponding register 143,an adder 144 for adding all the outputs of the m multipliers 142 forproviding an output w_(j) 0, and a function generator 145 for carryingout a predetermined conversion f to the output w0_(j) of adder 144 forproviding an output 140 (y_(j)) as the converted result. Weightcoefficient w0_(j1) -w0_(jm) of each register 143 is updated by thecorresponding weight calculating circuit 141. Each weight coefficient ofw0_(j1) -w0_(jm) is used in the process carried out in weightcalculating circuit 141 for the next learning.

The characteristic of function generator 145 is represented as y_(j) =f(w_(j)). Function f is a monotone non-decreasing function. In thepresent embodiment, a sigmoid function taking the value of intervals(0, 1) is employed as function f. Sigmoid function is defined by thefollowing equation (3). The operation of neuron 137 can be defined bythe following three equations. ##EQU1##

The above equation (3) representing the sigmoid function is shown in thegraph of FIG. 14. It can be appreciated from the graph of FIG. 14 that 0and 1 are approximated when w_(j) are -∞ and +∞, respectively. Whenw_(j) =0, 0.5 holds. Function f is not limited to this sigmoid function,and can take any other function that is a monotone non-decreasingfunction.

FIG. 15 is a block diagram of weight calculating circuit 141 forupdating the weight for the i-th input 139 (xi) of neuron 137. Oneneuron includes n weight calculating circuits 141.

Weight coefficient w0_(ji) corresponding to the i-th input x_(i) carriedout by weight calculating circuit 141 is calculated according to theoperation indicated by the following equation with output y_(j) of theoutput layer and teacher signal t_(j).

    ΔW0.sub.ji =ε·δ0.sub.j ·X.sub.i(4)

    where δ0.sub.j =(Y.sub.i -t.sub.j)y.sub.j (1-y.sub.j)(5)

ε : small positive real number

By adding Δw0_(ji) to weight coefficient w0_(ji) for the i-th inputx_(i) of the j-th neuron 137 of the output layer, weight coefficientw0_(ji) is updated. The updated weight coefficient w0_(ji) of register143 is multiplied by input x_(i) by multiplier 142 to be provided toadder 144 (refer to FIG. 13).

FIG. 15 is a block diagram of weight calculating circuit 141 forcarrying out the weight calculation indicated by equations (4) and (5).Referring to FIG. 15, weight calculating circuit 141 includes an adder146 for obtaining value (y_(j) -t_(j)) from output y_(j) of neuron 137and teacher signal t_(j) provided from decoder 130, an adder 147 forgenerating value (1-y_(j)) from output y_(j) of neuron 137, a multiplier148 for multiplying output y_(j) of neuron 137 by output (y_(j) -t_(j))of adder 136 by output (1-y_(j)) of adder 147 for providing value δ0_(j)shown in equation (5), a multiplier 150 for multiplying output δ0_(j) ofmultiplier 148 by the i-th input x_(i) by value (-ε) for calculatingvalue Δw0_(ji) shown in equation (4), and an adder 149 for obtaining thedifference between weight coefficient w0_(ji) of the i-th input x_(i)and output Δw0_(ji) of multiplier 150 for storing the same as weightcoefficient w0_(ji) in register 143.

Weight calculating circuit 141 further includes a multiplier 151 formultiplying output δ0_(j) of multiplier 148 by weight coefficientwo_(ji) provided from register 143 for providing the result tointermediate layer 132. The output δ0_(j) ·w0_(ji) of multiplier 151 isused for updating the weight coefficient in each neuron 131 ofintermediate layer 132.

A small positive real number value ε is used in the operation carriedout within weight calculating circuit 141. This value is obtainedempirically so as to improve the convergence properties of each weightcoefficient of the learning process.

FIG. 16 is a block diagram of weight updating circuit 129 for updatingthe weight coefficient of the conversion carried out at the i-th neuron131 included in intermediate layer 132. Each neuron 131 of intermediatelayer 132 is provided with one weight updating circuit 129.

Referring to FIG. 16, weight updating circuit 129 includes an adder 152for producing value (1-x_(i)) from output x_(i) of the i-th neuron 131,a multiplier 153 for producing value x_(i) (1-x_(i)) by multiplying theoutput of adder 152 by the output of neuron 131, and k weightcalculating circuits 154 for calculating the weight coefficient for theweight applied in neuron 131 for each of the k input signals of b₁-b_(k) (luminance average data a₀₀ -a₃₃). As shown in FIG. 16, eachweight calculating circuit 154 carries out weight calculation accordingto data for correction δ0₁ w0_(li) -δ0_(n) w0_(ni) provided from theneuron in output layer 136, the output of multiplier 153, thecorresponding input 134, and the respective weight coefficientcorresponding to the input included in neuron 131 for updating theweight coefficient.

FIG. 17 is a block diagram of the i-th neuron 131 of the plurality ofneuron 131 in intermediate layer 132. Referring to FIG. 17, neuron 131includes k registers 156 for storing each of weight coefficients w1_(il)-w1_(ik) corresponding to k inputs 134 (b₁ -b_(k)), k multipliers 155for multiplying the weight coefficient stored in the correspondingregister 156 by each of inputs 134 (b₁ -b_(k)), an adder 157 for takingthe total sum of the outputs of the k multipliers, and a functiongenerator 158 for obtaining output x_(i) by carrying out conversionindicated by the aforementioned sigmoid function f for output w1 ofadder 157.

As shown in FIG. 17, weight coefficient w1_(il) -w1_(ik) stored in eachregister 156 is updated by weight calculating circuit 154 (FIG. 16).Each weight coefficient w_(i1) -w1_(ik) is supplied to weightcalculating circuit 154 for respective updating.

FIG. 18 is a block diagram of weight calculating circuit 154 forcalculating the weight coefficient for each input. The number of weightcalculating circuits 154 corresponds to the number of inputs for neuron131. The weight calculating circuit shown in FIG. 18 is a circuit forcalculating weight coefficient w1_(ip) for the p-th input b_(p) for thei-th neuron 131. Referring to FIG. 18, weight calculating circuit 154includes n multipliers 161 for multiplying data δ0₁ w0_(li) -δ0_(n)w0_(ni) provided from output layer 136 by value x_(i) (1-x_(i)) providedfrom multiplier 160, an adder 162 for taking the total sum of theoutputs of the n multipliers 161 to obtain output δ1_(i), a multiplier163 for calculating the product of the p-th input b_(p), output δ1_(i)of adder 162 and the small positive real number ε to obtain outputΔw1_(ip), and an adder 164 for taking the difference between weightcoefficient w1_(ip) stored in register 156 and the output of multiplier163 for updating weight coefficient w1_(ip) of register 156 according tothe result thereof.

The update process of weight coefficient w1_(ip) by weight calculatingcircuit 154 is calculated by adding Δw1_(ip) obtained by the followingequation to weight coefficient w1_(ip).

    ΔW1.sub.ip =-ε·δ1.sub.i ·b.sub.p(6) ##EQU2##

The structure of ANN 107 is as follows. Referring to FIG. 5 again, ANN107 has the circuit configuration adapted by learning to obtain anoutput more represented by a teacher signal, by updating weightcoefficient w0_(ji) (j=1 to n, i=1 to m), w1_(ji) (j=1 to m, i=1 to k)in each of neurons 131 and 137 according to the teacher signal providedfrom A/D converter 115 for various inputs provided from area dividingand average producing circuit 106.

Switch 109 carries out selection of learning mode and normal operationmode of ANN 107, in addition to providing an offset value between theoutput of ANN 107 and the aperture value desired by the operator forproviding a teacher signal to ANN 107 at the time of learning mode. Theconnection of switch 109 to terminal 109b causes voltage 0 to be appliedto adder 114. Therefore, the output of D/A converter 108 is directlyprovided to comparator 112. When switch 109 is connected to terminal109a, a voltage predetermined by variable resistor 110 is applied toadder 114. This means that the output of adder 114 is offset from theoutput of D/A converter 108 by the voltage determined by variableresistor 110. By adjusting variable resistor 110 to obtain a diaphragmthe user feels optimum, a teacher signal is provided to ANN 107 viaadder 114 and A/D converter 115. The update of each weight coefficientof ANN 107 is carried out according to this teacher signal.

The above-video camera operates as follows. Lens 102 gathers incidentlight from object 101 to form an optical image of the object on thelight receiving plane of image sensor 104. The amount of light enteringimage sensor 104 is regulated by iris plate 103. Image sensor 104converts the optical image in the frame of a predetermined screen into avideo signal which is an electrical signal by photoelectric conversion.The video signal is provided to preamplifier 116 to be amplified. Theamplified signal is provided to a signal processing circuit not shown tobe converted into the system of television signal. The video signalprovided from preamplifier 116 is also supplied to A/D converter 105 ofautomatic iris control circuit 117.

A/D converter 105 converts the luminance value of the input video signalinto a digital value. This digital value is provided to area dividingand average producing circuit 106. The luminance data of the videosignal provided to area dividing and average producing circuit 106 isthe line sequential signal obtained by raster-scanning the twodimensional image formed on the light receiving plane of image sensor104.

Referring to FIG. 7, area dividing and average producing circuit 122generates area pulses h0-h3 and v0-v3 such as those shown in FIGS.8(B)-(E) and FIGS. 9(B)-(E), respectively. The area pulse is provided tointegrating circuits 121. Area dividing and average producing circuit122 generates and provides to each integrating circuit 121 a clearsignal CL for clearing the integration of the luminance data for eachvertical scanning area.

Referring to FIG. 10, integrating circuit 121 for integrating theluminance data of the video signal generated from sub-area a_(ij) shownin FIG. 6 operates as follows. Gate circuit 125 takes the AND of clocksignal CLK in synchronization with the input luminance data, verticalarea pulse v_(i), horizontal area pulse h_(j) to provide the same toregister 123. Register 123 is supplied with clock signal CLK only whenboth area pulses of v_(i) and h_(j) are at the high potential. Theluminance data provided from A/D converter 105 is added with the data inregister 123 by adder 124 to be provided to register 123. Register 123stores the output of adder 124 in synchronization with the clock signalprovided from gate circuit 125. By repeating the above-describedoperation, the luminance data of the video signal generated fromsub-area a_(ij) are integrated in register 123.

Clear signal CL is provided from area dividing and average producingcircuit 122 (refer to FIG. 7) to register 123 for each vertical period.In response to clear signal CL, the integrated value in register 123 iscleared for each one screen, whereby the luminance integrated value ofsub-area a_(ij) is provided as the output of register 123 for eachscreen.

As described above, the area of each sub-area a_(ij) shown in FIG. 6 isequal to each other, with the number of luminance data provided from asub-area also equal in number. Therefore, the integrated value providedfrom register 123 is proportional to the luminance average of thesub-area. The integrated value of the luminance data provided fromregister 123 can be taken as the luminance average of each sub-area. Aswill be described afterwards, each luminance average is given weightaccording to learning in ANN 107. It is therefore considered that eachsub-area is not necessarily equal to each other and that the luminanceaverage of each sub-area is not necessarily required to be used for iriscontrol. However, it is considered that the learning speed ofrecognition pattern by ANN 107 is improved with equal areas of eachsub-area, as in the present embodiment.

Referring to FIG. 11, luminance average data 134 provided from eachintegrating circuit 121 shown in FIG. 7 is branched in an input layernot shown of ANN 107 to be provided to each neuron 131 of intermediatelayer 132. Each neuron 131 carries out a predetermined conversion to theinput luminance average data 134. This result is provided to each neuron137 of output layer 136. The operation of each neuron 131 will bedescribed later.

Each neuron 137 of output layer 136 carries out a predeterminedconversion to the data provided from each neuron 131 of intermediatelayer 132. The converted data is provided to the positive input terminalof each corresponding comparator 135. The operation of each neuron 137will also be described later.

Each comparator 135 compares the output of the corresponding neuron 137with a reference value of 0.5. If the output of neuron 137 is 0.5 orabove, 1 is provided to encoder 133. If less than 0.5, 0 is provided toencoder 133. Encoder 133 responds to the output of each comparator 135to generate as an 8-bit data the number of the neuron 137 correspondingto the comparator 135 that provides 1. The generated 8-bit data isprovided to D/A converter 108 (refer to FIG. 5).

In normal operation, switch 109 is connected to terminal 109b. Voltageadder 114 is provided with the output of ANN 107 that has been convertedinto an analog value by D/A converter 108, and voltage 0. Therefore, theoutput of voltage adder 114 is a signal having the digital output of ANN107 converted into an analog data.

The output of voltage adder 114 is provided to the negative inputterminal of comparator 112. Comparator 112 compares reference voltage111 with the output of voltage adder 114 to control iris motor 113according to the comparison result. Accordingly, iris motor 113 operatesto open iris plate 103 when determination is made that the picture isdark, and operates to close iris plate 103 when determination is madethat the picture is too bright, from the result of the patternrecognition by ANN 107.

At the time of normal operation, the output of voltage adder 114 is theanalog data provided from ANN 107. If this data is digitized by A/Dconverter 115 and provided as teacher signal to ANN 107, update processof weight in ANN 107 will not be carried out since the teacher signaland the output of ANN 107 coincide with each other.

Referring to FIG. 17, neuron 131 of intermediate layer 132 carries outthe following operation for the input luminance average data 134. Eachmultiplier 155 multiplies the input luminance average data 134 (b_(p):p=1 to k) by weight coefficient w1_(i1) -w1_(ik) stored in eachregister 156 to provide the output to adder 157. Adder 157 obtains thetotal sum w1 of luminance average data b₁ -b_(k) given weight accordingto weight coefficients w1_(il) -w1_(ik) by multiplier 155. The total sumw1 is provided to function generator 158, where conversion according tosigmoid function shown in FIG. 14 is carried out. The converted resultis provided as intermediate layer output 139 (x_(i)).

Each neuron 131 of intermediate layer 132 carries out theabove-described process to provide outputs x₁ -x_(m) to each neuron 137in output layer 136.

Referring to FIG. 13, the j-th neuron 137 of the plurality of neurons137 in output layer 136 operates as follows. Outputs x₁ -x_(m) from eachneuron 131 of intermediate layer 132 are provided to the j-th neuron137. Each multiplier 142 in neuron 137 multiplies output 139 (x₁ -x_(m))of intermediate layer 132 by the corresponding weight coefficientw0_(j1) -w0_(jm) to provide the same to adder 144. Adder 144 providesthe total sum w0_(j) of the outputs of each multiplier 142 to functiongenerator 145. Function generator 145 carries out sigmoid conversionshown in FIG. 14 for output w0_(j) of adder 144 to obtain output y_(j).Output y_(j) is provided to the corresponding comparator 135 (refer toFIG. 11).

A similar process is carried out in all the neurons 137 in output layer136, whereby respective outputs y₁ -Y_(n) are provided to thecorresponding comparator 135.

More briefly, an output for controlling the operation of iris plate 103is obtained as the output of ANN 107, according to weighting andfunction conversion carried out in each neuron 131 of intermediate layer132, the weighting and function conversion carried out in each neuron137 of output layer 136, and the encoding of the number of the neuron ofoutput layer 136 which outputs 1. Each weight is recalculated by thelearning process of ANN 107 according to a teacher signal that will bedescribed afterwards, and updated appropriately. Thus, iris control iscarried out automatically reflecting for the learning based on theteacher signal as the output of ANN 107.

The operation of the video camera in the learning process of ANN 107will be described hereinafter. This learning employs errorbackpropagation rule, as mentioned before, to update each weightcoefficient according to equations (1)-(7). It is known that an outputclosely matching the teacher signal is obtained as the output of theneural network using the learning according to error backpropagationrule. It is also known that a learned neural network provides an outputclosely conforming to the liking of the user who provided the teachersignal even for inputs that are not yet learned.

The learning process is executed by switching switch 109 to terminal109a when the user makes determination that the aperture value obtainedusing ANN 107 is not optimum. The user adjusts the resistance ofvariable resistor 110 to obtain an optimum diaphragm. The voltageobtained by variable resistor 110 is applied to one terminal of voltageadder 114. This means that the output of voltage adder 114 is the analogvalue of the output of ANN 107 added with the voltage regulated byvariable resistor 110. In other words, the output of voltage adder 114has an offset with respect to the output of ANN 107. The iris controlvoltage provided from adder 114 is converted into a digital value by A/Dconverter 115 to be applied to ANN 107 as a teacher signal of, forexample, 8 bits.

Referring to FIG. 11, decoder 130 decodes the digital data of 8 bitsprovided from D/A converter 115 to generate 256 one bit teacher signalst0-t255. Each teacher signal is provided to the corresponding neuron 137of output layer 136. One of the teacher signals of t0-t255 takes a valueof 1, while the other remaining teacher signals all take a value of 0.

It is assumed that a luminance average data 134 is provided from areadividing and average producing circuit 106 from ANN 107 in the learningprocess. The output of output layer 136 is represented as a vector of256 dimensions expressed by equation (8).

    (y.sub.0, y.sub.1, . . . , y.sub.255)                      (8).

where y_(j) takes a value of 0-1.

It has been mentioned that the learning process is carried out accordingto error backpropagation rule. In other words, the update of the weightcoefficient is carried out from the stage of output layer 136 tointermediate layer 132 which is the direction opposite to that of theinput signal. Therefore, the process carried out in each neuron 137 ofoutput layer 136 will first be described hereinafter.

Referring to FIGS. 12-15, weight coefficient w0_(ji) corresponding tothe i-th output x_(i) provided from intermediate layer 132 of the j-thneuron 137 of output layer 136 is updated as follows. With particularreference to FIG. 15, value (y_(j) -t_(j)) is produced using outputy_(j) of the j-th neuron 137 and the j-th teacher signal t_(j) by adder146. Value (1-y_(j)) is produced by adder 147. Multiplier 148 multipliesthe outputs of adders 146 and 147 by output y_(j) of neuron 137 toproduce value δ0_(j) expressed by equation (5).

Multiplier 150 produces and provides to adder 149 the value Δw0_(ji)expressed by equation (4) by multiplying the provided small positivereal number (threshold value) ε by output δ0_(j) of multiplier 148 bythe i-th input x_(i). Adder 149 calculates and provides to register 143a new weight coefficient w0_(ji) by reducing output Δw0_(ji) ofmultiplier 150 from weight coefficient w0_(ji) stored in register 143.The update of the weight coefficient of neuron 137 is completed by thenew weight coefficient w0_(ji) being stored in register 143.

In multiplier 151 of weight calculating circuit 141, output δ0_(j) ofmultiplier 148 is multiplied by weight coefficient w0_(ji) to producevalue δ0_(j) ·w0_(ji) which is provided to intermediate layer 132. Thisvalue is used for updating the weight coefficient in the intermediatelayer.

Referring to FIGS. 16-18, the update process of weight coefficientcarried out in the i-th neuron 131 of intermediate layer 132 is carriedout as follows. It is assumed that output x_(i) is obtained as theoutput of the i-th neuron 131 for the input luminance average data 134(b₁ -b_(k)). With reference particularly to FIG. 18, adder 159 producesvalue (1-x_(i)) from output x_(i) of neuron 131 to provide the same tomultiplier 160. Multiplier 160 produces value x_(i) (1-x_(i)) fromoutput x_(i) of neuron 131 and the output of adder 159. This value isprovided to each weight calculating circuit 154.

The update process of weight coefficient w1_(ip) for the p-th luminanceaverage data b_(p) of the i-th neuron 131 is carried out as follows.With particular reference to FIG. 14, weight calculating circuit 154 isprovided with learning data δ0₁ w0_(li), δ0₂ w0_(2i), . . . , δ0_(n)w0_(ni) from each neuron 137 of output layer 136. In other words, eachweight calculating circuit 154 of the i-th neuron x_(i) is provided withdata δ0_(j) ·w0_(ji) (j=1 to n) provided from the weight calculatingcircuit of the weight coefficient for the i-th input to each neuron 137,out of the data from each neuron 137 of output data layer 136.

Each multiplier 161 multiplies the corresponding input δ0₁ w0_(1i)-δ0_(n) w0_(ni) by output x_(i) (1-x_(i)) of multiplier 160 to providethe same to adder 162. Adder 162 takes the total sum of the outputs ofmultiplier 161 to produce value δ1_(i) expressed by equation (7). Thisvalue is provided to multiplier 163.

Multiplier 163 multiplies the provided small positive real number ε byoutput δ1_(i) of adder 162 by the p-th luminance average data b_(p) toproduce and provide to adder 164 Δw1_(ip) expressed by equation (6).Adder 164 reads out the value of the corresponding weight coefficientw1_(ip) from register 156 to carry out operation with output Δw1_(ip) ofmultiplier 163 to produce a new weight coefficient w1_(ip) which isprovided to register 156. The update of weight coefficient w1_(ip) forthe p-th luminance average data b_(p) of the i-th neuron 131 ofintermediate layer 132 is completed by a new weight coefficient w1_(ip)being stored in register 156.

One cycle of learning is completed by carrying out the above describedupdate process of weight coefficient for all registers 143 in eachneuron 137 of output layer 136, and for all registers 156 in each neuron131 of intermediate layer 132.

Thus, ANN 107 is made adaptive to provide a more optimum diaphragm forthe object specified by the user from variable resistor 110 by repeatingthe learning of all weight coefficients in ANN 107 for various teachersignals.

Even in the case of an object state where optimum aperture value can notbe obtained by conventional automatic iris control using center-weightedmetering and foot-weighted metering, adjustment of the diaphragm iscarried out automatically conforming to the usage condition by adjustingthe diaphragm manually a few times. The disadvantage of having tomanually adjust the diaphragm frequently, as encountered in a videocamera having just a conventional manual iris control, is eliminated torealize a video camera having automatic iris control conforming to theusage purpose of the operator.

The network is input with the luminance of all angles of view, and avalue selected by the operator taking into consideration the balance ofluminance of the main object and the background scenery is provided asthe teacher signal to the network. According to this network, an iriscontrol signal is obtained for appropriately adjusting the luminancebalance of the main object and the background for all angles of view.

In the video camera that carries out automatic iris control using theabove-described neural network, the output of the neural network isalways fed back to the iris driving portion even when the operation modeis switched to the manual iris control mode by switch 109. There ispossibility of an unstable iris control if the weight coefficient in ANN107 is constantly updated. To avoid such a problem, only calculation ofupdating the weight coefficient of the neurons is carried out at thetime of manual iris control operation mode, wherein the contents of eachweight coefficient register in the neuron is updated when the operationmode is switched from manual to automatic iris control. This preventsthe generation of an unstable state of iris control occurring from theupdate of the coefficients.

The values learned by center-weighted metering and foot-weightedmetering is previously written in as the initial value of the weightcoefficient in ANN 107. Therefore, the problem does not occur of theoperation of the video camera becoming unstable when the video camera isused with automatic iris control right after the purchase of the videocamera.

In the above described embodiment, conversion was carried out so thatthe value of outputs y_(j), x_(i) become 0.5 when the values of w0_(j),w1_(i) supplied to the function generator is exactly 0, by the functionconversion carried out in each neuron. Each neuron should be implementedas follows if the input value to the function generator is a value otherthan 0 (threshold value) for an output value of 0.5. One terminal thatinputs a fixed value of "1" is added to each neuron. A new weightcoefficient is defined for this input, whereby this weight coefficientis multiplied by fixed value "1" to be provided to adders 144 and 157.The characteristics of each of function generators 145 and 158 is thatshifted by the threshold value in the left and right directions of thegraph of FIG. 10. This allows the provision of a threshold value foreach neuron. This weight coefficient may be updated any time by thelearning process.

Although the present invention has been described and illustrated indetail, it is clearly understood that the same is by way of illustrationand example only and is not to be taken by way of limitation, the spiritand scope of the present invention being limited only by the terms ofthe appended claims.

What is claimed is:
 1. A video camera comprising:optical means forgathering incident light from an object to form an image on apredetermined image information plane; incident light amount regulatingmeans, responsive to an incident light amount regulating signal, forregulating an incident light amount; image sensing means for imagesensing the image of the object within a predetermined frame formed bysaid optical means to provide a luminance signal; luminance distributioncharacteristics extraction means, connected to said image sensing means,for dividing said predetermined frame into a predetermined plurality ofsub-areas and for extracting a luminance distribution of the image insaid predetermined frame as a luminance value for each of said sub-areasaccording to said luminance signal to provide the extracted luminancedistribution as a plurality of luminance distribution signals, saidluminance distribution characteristics extraction means includingA/Dconverting means for A/D converting said luminance signal provided fromsaid image sensing means and for outputting a digital luminance dataindicating the luminance of the image, and average luminance calculatingmeans for integrating said digital luminance data over a predeterminedintegrating time period for each of said sub-areas to calculate anaverage luminance for each of said sub-areas as said luminancedistribution signals; adaptive means, coupled to said luminancedistribution characteristics extraction means and having an artificialneural network to which said plurality of luminance distribution signalsare input, for generating an aperture signal in accordance with apredetermined conversion, the predetermined conversion being madeadaptive so that an offset between a teacher signal and said aperturesignal is minimized; target value generating means, coupled to an outputof said adaptive means, for generating a target value signal fordetermining a target value of said incident light amount regulatingsignal; means for producing said incident light amount regulating signalaccording to said target value signal; and manually operable selectingmeans, responsive to operation by the user and operatively connected tosaid target value generating means, for selecting said aperture signalor an aperture signal as arbitrarily offset by the user as said targetvalue signal output from said target value generating means, said targetvalue signal being used to produce said teacher signal.
 2. The videocamera according to claim 1, wherein said artificial network of saidadaptive means comprises:an intermediate layer including a plurality offirst converting means, each for obtaining one output by performing afirst predetermined conversion of said luminance distribution signals;an output layer having a plurality of second converting means, each forobtaining one output by performing a second predetermined conversion ofan output of said intermediate layer; output converting means forperforming a third predetermined conversion of an output of said outputlayer to generate said aperture signal; and conversion updating meansfor adaptively updating said first and second predetermined conversionsaccording to the offset between said aperture signal output of saidartificial neural network and said teacher signal so that the offset isminimized.